/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
=======================================================================*/
package org.tensorflow.framework.losses;

import org.tensorflow.Operand;
import org.tensorflow.framework.losses.impl.AbstractLoss;
import org.tensorflow.framework.losses.impl.LossesHelper;
import org.tensorflow.op.Ops;
import org.tensorflow.types.family.TNumber;

/**
 * Computes the mean absolute percentage error between labels and predictions.
 *
 * <p><code>loss = 100 * abs(labels - predictions) / labels</code>
 *
 * <p>Standalone usage:
 *
 * <pre>
 *    Operand&lt;TFloat32&gt; labels =
 *        tf.constant(new float[][] {{2.f, 1.f}, {2.f, 3.f}});
 *    Operand&lt;TFloat32&gt; predictions =
 *        tf.constant(new float[][] {{1.f, 1.f}, {1.f, 0.f}});
 *    MeanAbsolutePercentageError mape = new MeanAbsolutePercentageError(tf);
 *    Operand&lt;TFloat32&gt; result = mape.call(Ops tf, labels, predictions);
 *    // produces 50f
 * </pre>
 *
 * <p>Calling with sample weight:
 *
 * <pre>
 *    Operand&lt;TFloat32&gt; sampleWeight = tf.constant(new float[] {0.7f, 0.3f});
 *    Operand&lt;TFloat32&gt; result = mape.call(Ops tf, labels, predictions, sampleWeight);
 *    // produces 20f
 * </pre>
 *
 * <p>Using <code>SUM</code> reduction type:
 *
 * <pre>
 *    MeanAbsolutePercentageError mape = new MeanAbsolutePercentageError(Reduction.SUM);
 *    Operand&lt;TFloat32&gt; result = mape.call(Ops tf, labels, predictions);
 *    // produces 100.0f
 * </pre>
 *
 * <p>Using <code>NONE</code> reduction type:
 *
 * <pre>
 *    MeanAbsolutePercentageError mape = new MeanAbsolutePercentageError(Reduction.NONE);
 *    Operand&lt;TFloat32&gt; result = mape.call(Ops tf, labels, predictions);
 *    // produces [25f, 75f]
 * </pre>
 */
public class MeanAbsolutePercentageError extends AbstractLoss {

  /**
   * Creates a MeanAbsolutePercentageError AbstractLoss using {@link Class#getSimpleName()} as the
   * loss name and a AbstractLoss Reduction of {@link AbstractLoss#REDUCTION_DEFAULT}
   */
  public MeanAbsolutePercentageError() {
    super();
  }

  /**
   * Creates a MeanAbsolutePercentageError AbstractLoss using {@link Class#getSimpleName()} as the
   * loss name
   *
   * @param reduction Type of Reduction to apply to the loss.
   */
  public MeanAbsolutePercentageError(Reduction reduction) {
    super(null, reduction);
  }

  /**
   * Creates a MeanAbsolutePercentageError
   *
   * @param name the name of the loss
   * @param reduction Type of Reduction to apply to the loss.
   */
  public MeanAbsolutePercentageError(String name, Reduction reduction) {
    super(name, reduction);
  }

  /** {@inheritDoc} */
  @Override
  public <T extends TNumber> Operand<T> call(
      Ops tf, Operand<? extends TNumber> labels, Operand<T> predictions, Operand<T> sampleWeights) {

    Operand<T> losses = Losses.meanAbsolutePercentageError(tf, labels, predictions);
    return LossesHelper.computeWeightedLoss(tf, losses, getReduction(), sampleWeights);
  }
}
